In the United States and in a number of other countries, a regulatory body like the FCC (Federal Communications Commission) oftentimes regulates and allocates the use of radio spectrum in order to fulfill the communications needs of entities such as businesses and local and state governments as well as individuals. More specifically, the FCC licenses a number of spectrum segments to entities and individuals for commercial or public use (“licensees”). These licensees may have an exclusive right to utilize their respective licensed spectrum segments for a particular geographical area for a certain amount of time. Such licensed spectrum segments are believed to be necessary in order to prevent or mitigate interference from other sources. However, if particular spectrum segments are not in use at a particular location at a particular time (“the available spectrum”), another device should be able to utilize such an available spectrum for communications. Such utilization of the available spectrum would make for a much more efficient use of the radio spectrum or portions thereof.
Previous spectrum-sensing techniques disclosed for determining the available spectrum have been met with resistance for at least two reasons: (1) they either do not work for sophisticated signal formats or (2) they require excessive hardware performances and/or computation power consumption. For example, a spectrum sensing technique has been disclosed where a non-coherent energy detector performs a computation of a Fast Fourier Transform (FFT) for a narrow-band input signal. The FFT provides the spectral components of the narrow-band input signal, which are then compared with a predetermined threshold level to detect a meaningful signal reception. However, this predetermined threshold level is highly-affected by unknown and varying noise levels. Moreover, the energy detector does not differentiate between modulated signals, noise, and interference signals. Thus, it does not work for sophisticated signal formats such as spread spectrum signal, frequency hopping, and multi-carrier modulation.
As another example, a cyclo-stationary feature detection technique has been disclosed as a spectrum-sensing technique that exploits the cyclic features of modulated signals, sinusoid carriers, periodic pulse trains, repetitive hopping patterns, cyclic prefixes, and the like. Spectrum correlation functions are calculated to detect the signal's unique features such as modulation types, symbol rates, and presence of interferers. Since the detection span and frequency resolution are trade-offs, the digital system upgrade is the only way to improve the detection resolution for the wideband input signal spectrum. However, such a digital system upgrade requires excessive hardware performances and computation power consumption. Further, flexible or scalable detection resolution is not available without any hardware changes.
Accordingly, there is a need in the industry for cognitive radios that allow the available spectrum to be utilized while minimizing hardware and power consumption requirements.